1 citations · 1 across the 7 of their papers we have counts for
9 papers
Exploring Causal Mechanisms with Generative Agent-Based Models
Xuan Liu, Haoyang Shang, Tanya Bhat +1
In this paper, we explore using generative agent-based models for a classical ABM application: testing how individual-level behavioral rules produce collective phenomena. We introd…
Validated Behavioral Hypotheses as a Lens for Evaluating Participant Simulation
Xuan Liu, HaoYang Shang, Zizhang Liu +5
We propose using validated behavioral hypotheses as a lens for evaluating LLM agents as simulated human participants. This approach makes behavioral agreement in participant simula…
Diagon: A Programmable Testbed for AI-Agent Cognitive Labor Markets
Xuan Liu, Haoyang Shang, Haojian Jin
AI agents are emerging as market participants that trade delegated cognitive work with one another on behalf of their users. Each agent can act as both a task poster and a contract…
Understanding Parents' Desires in Moderating Children's Interactions with GenAI Chatbots through LLM-Generated Probes
John Driscoll, Yulin Chen, Viki Shi +3
This paper studies how parents want to moderate children's interactions with Generative AI chatbots, with the goal of informing the design of future GenAI parental control tools. W…
General Modular Harness for LLM Agents in Multi-Turn Gaming Environments
Yuxuan Zhang, Haoyang Yu, Lanxiang Hu +2
We introduce a modular harness design for LLM agents that composes of perception, memory, and reasoning components, enabling a single LLM or VLM backbone to tackle a wide spectrum…
lmgame-Bench: How Good are LLMs at Playing Games?
Lanxiang Hu, Mingjia Huo, Yuxuan Zhang +6
Playing video games requires perception, memory, and planning, exactly the faculties modern large language model (LLM) agents are expected to master. We study the major challenges…
Decentralized Arena: Towards Democratic and Scalable Automatic Evaluation of Language Models
Yanbin Yin, Kun Zhou, Zhen Wang +11
The recent explosion of large language models (LLMs), each with its own general or specialized strengths, makes scalable, reliable benchmarking more urgent than ever. Standard prac…
GameArena: Evaluating LLM Reasoning through Live Computer Games
Lanxiang Hu, Qiyu Li, Anze Xie +4
Evaluating the reasoning abilities of large language models (LLMs) is challenging. Existing benchmarks often depend on static datasets, which are vulnerable to data contamination a…
On the Feasibility of Reasoning about the Internal States of Blackbox IoT Devices Using Side-Channel Information
Wei Sun, Yuwei Xiao, Haojian Jin +1
Internet of Things (IoT) devices are typically designed to function in a secure, closed environment, making it difficult for users to comprehend devices' behaviors. This paper show…